A Multi-Local Search-Based SHADE for Wind Farm Layout Optimization

Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However,...

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Veröffentlicht in:Electronics Jg. 13; H. 16; S. 3196
Hauptverfasser: Yang, Yifei, Tao, Sichen, Li, Haotian, Yang, Haichuan, Tang, Zheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 13.08.2024
Schlagworte:
Algorithms
Analysis
Buildings and facilities
Carbon
Clean technology
Climate change
Convergence
Efficiency
Electric power production
Emissions
Energy resources
Exploitation
Fossil fuels
Genetic algorithms
Global warming
Layouts
Mathematical optimization
Optimization
Optimization algorithms
Turbines
Wind farms
Wind power
Wind power generation
Wind turbines
ISSN:2079-9292, 2079-9292
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Zusammenfassung:Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However, in recent years, researchers have shifted towards enhancing continuous optimization algorithms and incorporating constraints to address WFLO challenges. This approach has shown remarkable promise, outperforming traditional genetic algorithms and gaining traction among researchers. To further elevate the performance of continuous optimization algorithms in the context of WFLO, we introduce a multi-local search-based SHADE, termed MS-SHADE. MS-SHADE is designed to fine-tune the trade-off between convergence speed and algorithmic diversity, reducing the likelihood of convergence stagnation in WFLO scenarios. To assess the effectiveness of MS-SHADE, we employed a more extensive and intricate wind condition model in our experiments. In a set of 16 problems, MS-SHADE’s average utilization efficiency improved by 0.14% compared to the best algorithm, while the optimal utilization efficiency increased by 0.3%. The results unequivocally demonstrate that MS-SHADE surpasses state-of-the-art WFLO algorithms by a significant margin.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13163196